This library implements the algorithm described in
Chakraborty, S., Vinodchandran, N. V., & Meel, K. S. (2022). Distinct Elements in Streams: An Algorithm for the (Text) Book. 6 pages, 727571 bytes. https://doi.org/10.4230/LIPIcs.ESA.2022.34
The accompanying article in Quanta is here: https://www.quantamagazine.org/computer-scientists-invent-an-efficient-new-way-to-count-20240516/
Binaries and installation instructions are available for x64 Linux, Apple Silicon and Intel, and x64 Windows in releases
You can also build your own if you have Rust installed: cargo install cvmcount.
cvmcount -t file.txt -e 0.8 -d 0.1 -s 2900-t --tokens: a valid path to a text file
-e --epsilon: how close you want your estimate to be to the true number of distinct tokens. A smaller ε means you require a more precise estimate. For example, ε = 0.05 means you want your estimate to be within 5 % of the actual value. An epsilon of 0.8 is a good starting point for most applications.
-d --delta: the level of certainty that the algorithm's estimate will fall within your desired accuracy range. A higher confidence (e.g. 99.9 %) means you're very sure the estimate will be accurate, while a lower confidence (e.g. 90 %) means there's a higher chance the estimate may be outside your desired range. A delta of 0.1 is a good starting point for most applications
-s --streamsize: this is used to determine buffer size and can be a loose approximation. The closer it is to the stream size, the more accurate the results
The --help option is available.
If you're thinking about using this library, you presumably know that it only provides an estimate (within the specified bounds), similar to something like HyperLogLog. You are trading accuracy for speed!
Calculating the unique tokens in a 418K UTF-8 text file takes 18.6 ms ± 0.3 ms on an M2 Pro
This library strips punctuation from input tokens using a regex. I assume there is a small performance penalty, but it seems like a small price to pay for increased practicality.